QoS-Aware Resource Allocation for Mobile Edge Networks: User Association, Precoding and Power Allocation

Mobile edge computing (MEC) can provide computing and storage services to user equipments (UEs) by utilizing edge nodes known as the small base stations (SBS's) deployed at the edge of the network. The short-distance transmission nature between SBS's and UEs makes the millimeter-wave (mmWa...

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Vydáno v:IEEE transactions on vehicular technology Ročník 70; číslo 12; s. 12617 - 12630
Hlavní autoři: Niu, Guanchong, Cao, Qi, Pun, Man-On
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Array signal processing
Computer simulation
D.C. programming
Edge computing
hybrid beamforming
Interference
Iterative algorithms
Iterative methods
Millimeter waves
Mobile computing
Mobile edge computing
Optimization
power allocation
Precoding
Quality of service
Radio equipment
Resource allocation
Resource management
Search algorithms
user association
ISSN:0018-9545, 1939-9359
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Shrnutí:Mobile edge computing (MEC) can provide computing and storage services to user equipments (UEs) by utilizing edge nodes known as the small base stations (SBS's) deployed at the edge of the network. The short-distance transmission nature between SBS's and UEs makes the millimeter-wave (mmWave) communication empowered with multiple-input multiple-output (MIMO) hybrid precoding techniques particularly attractive for MEC. In this work, we consider the UE-SBS association, precoding design and power allocation for MEC networks endowed with mmWave MIMO. More specifically, the user association problem is first formulated as a max-k-cut (M <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> C) problem and then, solved by a distributed local-search algorithm. Next, the joint optimization of precoding and power allocation is cast into the difference of two convex functions (D.C.) programming framework before an iterative rank-constrained D.C. programming algorithm is developed to maximize the weighted sum-rate (WSR) of all UEs while taking into account the quality of service (QoS) requirement of each UE. Furthermore, the monotonic convergence of the proposed iterative algorithm is analytically proven. Finally, extensive computer simulation is conducted to demonstrate the effectiveness of the proposed iterative algorithm.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3076353